The purpose of this study is to understand the phenotypes of thyroid eye disease (TED) through data derived from a multiatlas segmentation of computed tomography (CT) imaging. Images of 170 orbits of 85 retrospectively selected TED patients were analyzed with the developed automated segmentation tool. Twenty-five bilateral orbital structural metrics were used to perform principal component analysis (PCA). PCA of the 25 structural metrics identified the two most dominant structural phenotypes or characteristics, the “big volume phenotype” and the “stretched optic nerve phenotype,” that accounted for 60% of the variance. Most of the subjects in the study have either of these characteristics or a combination of both. A Kendall rank correlation between the principal components (phenotypes) and clinical data showed that the big volume phenotype was very strongly correlated (p-value <0.05) with motility defects, and loss of visual acuity. Whereas, the stretched optic nerve phenotype was strongly correlated (p-value <0.05) with an increased Hertel measurement, relatively better visual acuity, and smoking. Two clinical subtypes of TED, type 1 with enlarged muscles and type 2 with proptosis, are recognizable in CT imaging. Our automated algorithm identifies the phenotypes and finds associations with clinical markers.

<strong>Background:</strong> Clustering thalamic nuclei is important for both research and clinical purposes. For example, ventral intermediate nuclei in thalami serve as targets in both deep brain stimulation neurosurgery and radiosurgery for treating patients suffering from movement disorders (e.g., Parkinson's disease and essential tremor). Diffusion magnetic resonance imaging (dMRI) is able to reflect tissue microstructure in the central nervous system via fitting different models, such as, the diffusion tensor (DT), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI), diffusion kurtosis imaging (DKI) and the spherical mean technique (SMT). <strong>Purpose:</strong> To test which of the above-mentioned dMRI models is better for thalamic parcellation, we proposed a framework of <i>k</i>-means clustering, implemented it on each model, and evaluated the agreement with histology. <strong>Method:</strong> An ex vivo monkey brain was scanned in a 9.4T MRI scanner at 0.3mm resolution with b values of 3000, 6000, 9000 and 12000 s/mm<sup>2</sup>. <i>K</i>-means clustering on each thalamus was implemented using maps of dMRI models fitted to the same data. Meanwhile, histological nuclei were identified by AChE and Nissl stains of the same brain. Overall agreement rate and agreement rate for each nucleus were calculated between clustering and histology. Sixteen thalamic nuclei on each hemisphere were included. <strong>Results:</strong> Clustering with the DKI model has slightly higher overall agreement rate but clustering with other dMRI models result in higher agreement rate in some nuclei. <strong>Conclusion:</strong> dMRl models should be carefully selected to better parcellate the thalamus, depending on the specific purpose of the parcellation.

Gradient coils in magnetic resonance imaging do not produce perfectly linear gradient fields. For diffusion imaging, the field nonlinearities cause the amplitude and direction of the applied diffusion gradients to vary over the field of view. This leads to site- and scan-specific systematic errors in estimated diffusion parameters such as diffusivity and anisotropy, reducing reliability especially in studies that take place over multiple sites. These errors can be substantially reduced if the actual scanner-specific gradient coil magnetic fields are known. The nonlinearity of the coil fields is measured by scanner manufacturers and used internally for geometric corrections, but obtaining and using the information for a specific scanner may be impractical for many sites that operate without special-purpose local engineering and research support. We have implemented an empirical field-mapping procedure using a large phantom combined with a solid harmonic approximation to the coil fields that is simple to perform and apply. Here we describe the accuracy and precision of the approach in reproducing manufacturer gold standard field maps and in reducing spatially varying errors in quantitative diffusion imaging for a specific scanner. Before correction, median B value error ranged from 33 - 41 relative to manufacturer specification at 100 mm from isocenter; correction reduced this to 0 - 4. On-axis spatial variation in the estimated mean diffusivity of an isotropic phantom was 2.2% - 4.1% within 60 mm of isocenter before correction, 0.5% - 1.6% after. Expected fractional anisotropy in the phantom was 0; highest estimated fractional anisotropy within 60 mm of isocenter was reduced from 0.024 to 0.012 in the phase encoding direction (48% reduction) and from 0.020 to 0.006 in the frequency encoding direction (72% reduction).

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework’s performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop and HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available.

The diffusion tensor model is nonspecific in regions where micrometer structural patterns are inconsistent at the millimeter scale (i.e., brain regions with pathways that cross, bend, branch, fan, etc.). Numerous models have been proposed to represent crossing fibers and complex intravoxel structure from in vivo diffusion weighted magnetic resonance imaging (e.g., high angular resolution diffusion imaging—HARDI). Here, we present an empirical comparison of two HARDI approaches—persistent angular structure MRI (PAS-MRI) and Q-ball—using a newly acquired reproducibility dataset. Briefly, a single subject was scanned 11 times with 96 diffusion weighted directions and 10 reference volumes for each of two b values (1000 and 3000 s / mm2 for a total of 2144 volumes). Empirical reproducibility of intravoxel fiber fractions (number/strength of peaks), angular orientation, and fractional anisotropy was compared with metrics from a traditional tensor analysis approach, focusing on b values of 1000 and 3000 s / mm2. PAS-MRI is shown to be more reproducible than Q-ball and offers advantages at low b values. However, there are substantial and biologically meaningful differences between the intravoxel structures estimated both in terms of analysis method as well as by b value. The two methods suggest a fundamentally different microarchitecture of the human brain; therefore, it is premature to perform meta-analysis or combine results across HARDI studies using a different analysis model or acquisition sequences.

Whole brain segmentation and cortical surface parcellation are essential in understanding the brain’s anatomicalfunctional relationships. Multi-atlas segmentation has been regarded as one of the leading segmentation methods for the whole brain segmentation. In our recent work, the multi-atlas technique has been adapted to surface reconstruction using a method called Multi-atlas CRUISE (MaCRUISE). The MaCRUISE method not only performed consistent volumesurface analyses but also showed advantages on robustness compared with the FreeSurfer method. However, a detailed surface parcellation was not provided by MaCRUISE, which hindered the region of interest (ROI) based analyses on surfaces. Herein, the MaCRUISE surface parcellation (MaCRUISEsp) method is proposed to perform the surface parcellation upon the inner, central and outer surfaces that are reconstructed from MaCRUISE. MaCRUISEsp parcellates inner, central and outer surfaces with 98 cortical labels respectively using a volume segmentation based surface parcellation (VSBSP), following a topological correction step. To validate the performance of MaCRUISEsp, 21 scanrescan magnetic resonance imaging (MRI) T1 volume pairs from the Kirby21 dataset were used to perform a reproducibility analyses. MaCRUISEsp achieved 0.948 on median Dice Similarity Coefficient (DSC) for central surfaces. Meanwhile, FreeSurfer achieved 0.905 DSC for inner surfaces and 0.881 DSC for outer surfaces, while the proposed method achieved 0.929 DSC for inner surfaces and 0.835 DSC for outer surfaces. Qualitatively, the results are encouraging, but are not directly comparable as the two approaches use different definitions of cortical labels.

High Angular Resolution Diffusion Imaging (HARDI) models are used to capture complex intra-voxel microarchitectures. The magnetic resonance imaging sequences that are sensitized to diffusion are often highly accelerated and prone to motion, physiologic, and imaging artifacts. In diffusion tensor imaging, robust statistical approaches have been shown to greatly reduce these adverse factors without human intervention. Similar approaches would be possible with HARDI methods, but robust versions of each distinct HARDI approach would be necessary. To avoid the computational and pragmatic burdens of creating individual robust HARDI analysis variants, we propose a robust outlier imputation model to mitigate outliers prior to traditional HARDI analysis. This model uses a weighted spherical harmonic fit of diffusion weighted magnetic resonance imaging scans to estimate the values which had been corrupted during acquisition to restore them. Briefly, spherical harmonics of 6th order were used to generate basis function which were weighted by diffusion signal for detection of outliers. For validation, a single healthy volunteer was scanned for a single session comprising of two scans one without head movement and the other with deliberate head movement at a b-value of 3000 s/mm<sup>2</sup> with 64 diffusion weighted directions with a single b0 (5 averages) per scan. The deliberate motion from the volunteer created natural artifacts in the acquisition of one of the scans. The imputation model shows reduction in root mean squared error of the raw signal intensities and improvement for the HARDI method Q-ball in terms of the Angular Correlation Coefficient. The results reveal that there is quantitative and qualitative improvement. The proposed model can be used as general pre-processing model before implementing any HARDI model in general to restore the artifacts which are created because of the outlier diffusion signal in certain gradient volumes.

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI’s with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI’s acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p &lt; 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI’s with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1

The choice of surface template plays an important role in cross-sectional subject analyses involving cortical brain surfaces because there is a tendency toward registration bias given variations in inter-individual and inter-group sulcal and gyral patterns. In order to account for the bias and spatial smoothing, we propose a feature-based unbiased average template surface. In contrast to prior approaches, we factor in the sample population covariance and assign weights based on feature information to minimize the influence of covariance in the sampled population. The mean surface is computed by applying the weights obtained from an inverse covariance matrix, which guarantees that multiple representations from similar groups (e.g., involving imaging, demographic, diagnosis information) are down-weighted to yield an unbiased mean in feature space. Results are validated by applying this approach in two different applications. For evaluation, the proposed unbiased weighted surface mean is compared with un-weighted means both qualitatively and quantitatively (mean squared error and absolute relative distance of both the means with baseline). In first application, we validated the stability of the proposed optimal mean on a scan-rescan reproducibility dataset by incrementally adding duplicate subjects. In the second application, we used clinical research data to evaluate the difference between the weighted and unweighted mean when different number of subjects were included in control versus schizophrenia groups. In both cases, the proposed method achieved greater stability that indicated reduced impacts of sampling bias. The weighted mean is built based on covariance information in feature space as opposed to spatial location, thus making this a generic approach to be applicable to any feature of interest.

An understanding of the bias and variance of diffusion weighted magnetic resonance imaging (DW-MRI) acquisitions across scanners, study sites, or over time is essential for the incorporation of multiple data sources into a single clinical study. Studies that combine samples from various sites may be introducing confounding factors due to site-specific artifacts and patterns. Differences in bias and variance across sites may render the scans incomparable, and, without correction, inferences obtained from these data may be misleading. We present an analysis of the bias and variance of scans of the same subjects across different sites and evaluate their impact on statistical analyses. In previous work, we presented a simulation extrapolation (SIMEX) technique for bias estimation as well as a wild bootstrap technique for variance estimation in metrics obtained from a Q-ball imaging (QBI) reconstruction of empirical high angular resolution diffusion imaging (HARDI) data. We now apply those techniques to data acquired from 5 healthy volunteers on 3 independent scanners under closely matched acquisition protocols. The bias and variance of GFA measurements were estimated on a voxel-wise basis for each scan and compared across study sites to identify site-specific differences. Further, we provide model recommendations that can be used to determine the extent of the impact of bias and variance as well as aspects of the analysis to account for these differences. We include a decision tree to help researchers determine if model adjustments are necessary based on the bias and variance results.

Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods. In this paper, we propose the Splenomegaly Segmentation Network (SSNet) to address spatial variations when segmenting extraordinarily large spleens. SSNet was designed based on the framework of image-to-image conditional generative adversarial networks (cGAN). Specifically, the Global Convolutional Network (GCN) was used as the generator to reduce false negatives, while the Markovian discriminator (PatchGAN) was used to alleviate false positives. A cohort of clinically acquired 3D MRI scans (both T1 weighted and T2 weighted) from patients with splenomegaly were used to train and test the networks. The experimental results demonstrated that a mean Dice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNet on independently tested MRI volumes of patients with splenomegaly.

Sulcal depth is an important marker of brain anatomy in neuroscience/neurological function. Previously, sulcal depth has been explored at the region-of-interest (ROI) level to increase statistical sensitivity to group differences. In this paper, we present a fully automated method that enables inferences of ROI properties from a sulcal region- focused perspective consisting of two main components: 1) sulcal depth computation and 2) sulcal curve-based refined ROIs. In conventional statistical analysis, the average sulcal depth measurements are employed in several ROIs of the cortical surface. However, taking the average sulcal depth over the full ROI blurs overall sulcal depth measurements which may result in reduced sensitivity to detect sulcal depth changes in neurological and psychiatric disorders. To overcome such a blurring effect, we focus on sulcal fundic regions in each ROI by filtering out other gyral regions. Consequently, the proposed method results in more sensitive to group differences than a traditional ROI approach. In the experiment, we focused on a cortical morphological analysis to sulcal depth reduction in schizophrenia with a comparison to the normal healthy control group. We show that the proposed method is more sensitivity to abnormalities of sulcal depth in schizophrenia; sulcal depth is significantly smaller in most cortical lobes in schizophrenia compared to healthy controls (p &lt; 0.05).

Image registration involves identification of a transformation to fit a target image to a reference image space. The success of the registration process is vital for correct interpretation of the results of many medical image-processing applications, including multi-atlas segmentation. While there are several validation metrics employed in rigid registration to examine the accuracy of the method, non-rigid registrations (NRR) are validated subjectively in most cases, validated in offline cases, or based on image similarity metrics, all of which have been shown to poorly correlate with true registration quality. In this paper, we model the error for each target scan by expanding on the idea of Assessing Quality Using Image Registration Circuits (AQUIRC), which created a model for error “quality” associated with NRR. In this paper, we model the Dice similarity coefficient (DSC) error in the network, for a more interpretable measure. We test four functional models using a leave-one-out strategy to evaluate the relationship between edge DSC and circuit DSC: linear, quadratic, third order, or multiplicative models. We found that the quadratic model most accurately learns the NRR-DSC, with a median correlation coefficient of 0.58 with the true NRR-DSC, we call this the QUADRATIC (QUAlity of Dice in RegistrATIon Circuits) model. The QUADRATIC model is used for multi-atlas segmentation based on majority vote. Choosing the four best atlases predicted from the QUDRATIC model resulted in a 7% increase in the DSC between segmented image and true labels.

An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease or markers of development in populations. Having a low-dimensional manifold of an image allows for easier statistical comparisons between groups and the synthesis of group representatives. Previous studies have sought to identify the best mapping of brain MRI to a low-dimensional manifold, but have been limited by assumptions of explicit similarity measures. In this work, we use deep learning techniques to investigate implicit manifolds of normal brains and generate new, high-quality images. We explore implicit manifolds by addressing the problems of image synthesis and image denoising as important tools in manifold learning. First, we propose the unsupervised synthesis of T1-weighted brain MRI using a Generative Adversarial Network (GAN) by learning from 528 examples of 2D axial slices of brain MRI. Synthesized images were first shown to be unique by performing a cross-correlation with the training set. Real and synthesized images were then assessed in a blinded manner by two imaging experts providing an image quality score of 1-5. The quality score of the synthetic image showed substantial overlap with that of the real images. Moreover, we use an autoencoder with skip connections for image denoising, showing that the proposed method results in higher PSNR than FSL SUSAN after denoising. This work shows the power of artificial networks to synthesize realistic imaging data, which can be used to improve image processing techniques and provide a quantitative framework to structural changes in the brain.

Multiatlas segmentation offers an exceedingly convenient process by which image segmentation tools can be created from a series of labeled atlases (i.e., raters). However, creation of the atlases is exceedingly time consuming and prone to shifts in clinical/research demands as anatomical definitions are refined, combined, or subdivided. Hence, a process by which atlases from distinct, but complementary, anatomical “protocols” could be combined would allow for greater innovation in structural analysis and efficiency of data (re)use. Recent innovation in protocol fusion has shown that propagation of information across distinct protocols is feasible. However, how to effectively include this information in simultaneous truth and performance level estimation (STAPLE) has been elusive. We present a generalization of the STAPLE framework to account for multiprotocol rater performance (i.e., accuracy of registered atlases). This approach, multiset STAPLE (MS-STAPLE), provides a statistical framework for combining label information from atlases that have been labeled with distinct protocols (i.e., whole brain versus subcortical) and is compatible with the current local, nonlocal, probabilistic, log-odds, and hierarchical innovations in STAPLE theory. Using the MS-STAPLE approach, information from a broad range of datasets can be combined so that each available dataset contributes in a spatially dependent manner to local labels. We evaluate the model in simulations and in the context of an experiment where an existing set of whole-brain labels (14 structures) is refined to include parcellation of subcortical structures (26 structures). In the empirical results, we see significant improvement in the Dice similarity coefficient when comparing MS-STAPLE to STAPLE and nonlocal MS-STAPLE to nonlocal STAPLE.

Large scale image processing demands a standardized way of not only storage but also a method for job distribution and scheduling. The eXtensible Neuroimaging Archive Toolkit (XNAT) is one of several platforms that seeks to solve the storage issues. Distributed Automation for XNAT (DAX) is a job control and distribution manager. Recent massive data projects have revealed several bottlenecks for projects with &lt;100,000 assessors (i.e., data processing pipelines in XNAT). In order to address these concerns, we have developed a new API, which exposes a direct connection to the database rather than REST API calls to accomplish the generation of assessors. This method, consistent with XNAT, keeps a full history for auditing purposes. Additionally, we have optimized DAX to keep track of processing status on disk (called DISKQ) rather than on XNAT, which greatly reduces load on XNAT by vastly dropping the number of API calls. Finally, we have integrated DAX into a Docker container with the idea of using it as a Docker controller to launch Docker containers of image processing pipelines. Using our new API, we reduced the time to create 1,000 assessors (a sub-cohort of our case project) from 65040 seconds to 229 seconds (a decrease of over 270 fold). DISKQ, using pyXnat, allows launching of 400 jobs in under 10 seconds which previously took 2,000 seconds. Together these updates position DAX to support projects with hundreds of thousands of scans and to run them in a time-efficient manner.

Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512&times;512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

We examine imaging and electronic medical records (EMR) of 588 subjects over five major disease groups that affect optic nerve function. An objective evaluation of the role of imaging and EMR data in diagnosis of these conditions would improve understanding of these diseases and help in early intervention. We developed an automated image processing pipeline that identifies the orbital structures within the human eyes from computed tomography (CT) scans, calculates structural size, and performs volume measurements. We customized the EMR-based phenome-wide association study (PheWAS) to derive diagnostic EMR phenotypes that occur at least two years prior to the onset of the conditions of interest from a separate cohort of 28,411 ophthalmology patients. We used random forest classifiers to evaluate the predictive power of image-derived markers, EMR phenotypes, and clinical visual assessments in identifying disease cohorts from a control group of 763 patients without optic nerve disease. Image-derived markers showed more predictive power than clinical visual assessments or EMR phenotypes. However, the addition of EMR phenotypes to the imaging markers improves the classification accuracy against controls: the AUC improved from 0.67 to 0.88 for glaucoma, 0.73 to 0.78 for intrinsic optic nerve disease, 0.72 to 0.76 for optic nerve edema, 0.72 to 0.77 for orbital inflammation, and 0.81 to 0.85 for thyroid eye disease. This study illustrates the importance of diagnostic context for interpretation of image-derived markers and the proposed PheWAS technique provides a flexible approach for learning salient features of patient history and incorporating these data into traditional machine learning analyses.

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., “short” processing times and/or “large” datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply “large scale” processing transitions into “big data” and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and nonrelevant for medical imaging.

In magnetic resonance diffusion imaging, gradient nonlinearity causes significant bias in the estimation of quantitative diffusion parameters such as diffusivity, anisotropy, and diffusion direction in areas away from the magnet isocenter. This bias can be substantially reduced if the scanner- and coil-specific gradient field nonlinearities are known. Using a set of field map calibration scans on a large (29 cm diameter) phantom combined with a solid harmonic approximation of the gradient fields, we predicted the obtained b-values and applied gradient directions throughout a typical field of view for brain imaging for a typical 32-direction diffusion imaging sequence. We measured the stability of these predictions over time. At 80 mm from scanner isocenter, predicted b-value was 1-6% different than intended due to gradient nonlinearity, and predicted gradient directions were in error by up to 1 degree. Over the course of one month the change in these quantities due to calibration-related factors such as scanner drift and variation in phantom placement was &lt;0.5% for b-values, and &lt;0.5 degrees for angular deviation. The proposed calibration procedure allows the estimation of gradient nonlinearity to correct b-values and gradient directions ahead of advanced diffusion image processing for high angular resolution data, and requires only a five-minute phantom scan that can be included in a weekly or monthly quality assurance protocol.

Non-invasive spleen volume estimation is essential in detecting splenomegaly. Magnetic resonance imaging (MRI) has
been used to facilitate splenomegaly diagnosis in vivo. However, achieving accurate spleen volume estimation from MR
images is challenging given the great inter-subject variance of human abdomens and wide variety of clinical
images/modalities. Multi-atlas segmentation has been shown to be a promising approach to handle heterogeneous data and
difficult anatomical scenarios. In this paper, we propose to use multi-atlas segmentation frameworks for MRI spleen
segmentation for splenomegaly. To the best of our knowledge, this is the first work that integrates multi-atlas segmentation
for splenomegaly as seen on MRI. To address the particular concerns of spleen MRI, automated and novel semi-automated
atlas selection approaches are introduced. The automated approach interactively selects a subset of atlases using selective
and iterative method for performance level estimation (SIMPLE) approach. To further control the outliers, semi-automated
craniocaudal length based SIMPLE atlas selection (L-SIMPLE) is proposed to introduce a spatial prior in a fashion to
guide the iterative atlas selection. A dataset from a clinical trial containing 55 MRI volumes (28 T1 weighted and 27 T2
weighted) was used to evaluate different methods. Both automated and semi-automated methods achieved median DSC &gt;
0.9. The outliers were alleviated by the L-SIMPLE (&asymp;1 min manual efforts per scan), which achieved 0.9713 Pearson
correlation compared with the manual segmentation. The results demonstrated that the multi-atlas segmentation is able to
achieve accurate spleen segmentation from the multi-contrast splenomegaly MRI scans.

Crossing fibers are prevalent in human brains and a subject of intense interest for neuroscience. Diffusion tensor imaging (DTI) can resolve tissue orientation but is blind to crossing fibers. Many advanced diffusion-weighted magnetic resolution imaging (MRI) approaches have been presented to extract crossing-fibers from high angular resolution diffusion imaging (HARDI), but the relative sensitivity and specificity of approaches remains unclear. Here, we examine two leading approaches (PAS and q-ball) in the context of a large-scale, single subject reproducibility study. A single healthy individual was scanned 11 times with 96 diffusion weighted directions and 10 reference volumes for each of five b-values (1000, 1500, 2000, 2500, 3000 s/mm2) for a total of 5830 volumes (over the course of three sessions). We examined the reproducibility of the number of fibers per voxel, volume fraction, and crossing-fiber angles. For each method, we determined the minimum resolvable angle for each acquisition. Reproducibility of fiber counts per voxel was generally high (~80% consensus for PAS and ~70% for q-ball), but there was substantial bias between individual repetitions and model estimated with all data (~10% lower consensus for PAS and ~15% lower for q-ball). Both PAS and q-ball predominantly discovered fibers crossing at near 90 degrees, but reproducibility was higher for PAS across most measures. Within voxels with low anisotropy, q-ball finds more intra-voxel structure; meanwhile, PAS resolves multiple fibers at greater than 75 degrees for more voxels. These results can inform researchers when deciding between HARDI approaches or interpreting findings across studies.

The basal ganglia and limbic system, particularly the thalamus, putamen, internal and external globus pallidus, substantia
nigra, and sub-thalamic nucleus, comprise a clinically relevant signal network for Parkinson’s disease. In order to manually
trace these structures, a combination of high-resolution and specialized sequences at 7T are used, but it is not feasible to
scan clinical patients in those scanners. Targeted imaging sequences at 3T such as F-GATIR, and other optimized inversion
recovery sequences, have been presented which enhance contrast in a select group of these structures. In this work, we
show that a series of atlases generated at 7T can be used to accurately segment these structures at 3T using a combination
of standard and optimized imaging sequences, though no one approach provided the best result across all structures. In the
thalamus and putamen, a median Dice coefficient over 0.88 and a mean surface distance less than 1.0mm was achieved
using a combination of T1 and an optimized inversion recovery imaging sequences. In the internal and external globus
pallidus a Dice over 0.75 and a mean surface distance less than 1.2mm was achieved using a combination of T1 and FGATIR
imaging sequences. In the substantia nigra and sub-thalamic nucleus a Dice coefficient of over 0.6 and a mean
surface distance of less than 1.0mm was achieved using the optimized inversion recovery imaging sequence. On average,
using T1 and optimized inversion recovery together produced significantly improved segmentation results than any
individual modality (p&lt;0.05 wilcox sign-rank test).

Known for its distinct role in memory, the hippocampus is one of the most studied regions of the brain. Recent advances
in magnetic resonance imaging have allowed for high-contrast, reproducible imaging of the hippocampus. Typically, a
trained rater takes 45 minutes to manually trace the hippocampus and delineate the anterior from the posterior segment at
millimeter resolution. As a result, there has been a significant desire for automated and robust segmentation of the
hippocampus. In this work we use a population of 195 atlases based on T1-weighted MR images with the left and right
hippocampus delineated into the head and body. We initialize the multi-atlas segmentation to a region directly around each
lateralized hippocampus to both speed up and improve the accuracy of registration. This initialization allows for
incorporation of nearly 200 atlases, an accomplishment which would typically involve hundreds of hours of computation
per target image. The proposed segmentation results in a Dice similiarity coefficient over 0.9 for the full hippocampus.
This result outperforms a multi-atlas segmentation using the BrainCOLOR atlases (Dice 0.85) and FreeSurfer (Dice 0.75).
Furthermore, the head and body delineation resulted in a Dice coefficient over 0.87 for both structures. The head and body
volume measurements also show high reproducibility on the Kirby 21 reproducibility population (R<sup>2</sup> greater than 0.95, p
&lt; 0.05 for all structures). This work signifies the first result in an ongoing work to develop a robust tool for measurement
of the hippocampus and other temporal lobe structures.

Automatic spleen segmentation on CT is challenging due to the complexity of abdominal structures. Multi-atlas
segmentation (MAS) has shown to be a promising approach to conduct spleen segmentation. To deal with the
substantial registration errors between the heterogeneous abdominal CT images, the context learning method for
performance level estimation (CLSIMPLE) method was previously proposed. The context learning method
generates a probability map for a target image using a Gaussian mixture model (GMM) as the prior in a Bayesian
framework. However, the CLSSIMPLE typically trains a single GMM from the entire heterogeneous training atlas
set. Therefore, the estimated spatial prior maps might not represent specific target images accurately. Rather than
using all training atlases, we propose an adaptive GMM based context learning technique (AGMMCL) to train the
GMM adaptively using subsets of the training data with the subsets tailored for different target images. Training sets
are selected adaptively based on the similarity between atlases and the target images using cranio-caudal length,
which is derived manually from the target image. To validate the proposed method, a heterogeneous dataset with a
large variation of spleen sizes (100 cc to 9000 cc) is used. We designate a metric of size to differentiate each group
of spleens, with 0 to 100 cc as small, 200 to 500cc as medium, 500 to 1000 cc as large, 1000 to 2000 cc as XL, and
2000 and above as XXL. From the results, AGMMCL leads to more accurate spleen segmentations by training
GMMs adaptively for different target images.

The optic nerve (ON) is a vital structure in the human visual system and transports all visual information from the retina
to the cortex for higher order processing. Due to the lack of redundancy in the visual pathway, measures of ON damage
have been shown to correlate well with visual deficits. These measures are typically taken at an arbitrary anatomically
defined point along the nerve and do not characterize changes along the length of the ON. We propose a fully automated,
three-dimensionally consistent technique building upon a previous independent slice-wise technique to estimate the radius
of the ON and surrounding cerebrospinal fluid (CSF) on high-resolution heavily T2-weighted isotropic MRI. We show
that by constraining results to be three-dimensionally consistent this technique produces more anatomically viable results.
We compare this technique with the previously published slice-wise technique using a short-term reproducibility data set,
10 subjects, follow-up &lt;1 month, and show that the new method is more reproducible in the center of the ON. The center
of the ON contains the most accurate imaging because it lacks confounders such as motion and frontal lobe interference.
Long-term reproducibility, 5 subjects, follow-up of approximately 11 months, is also investigated with this new technique
and shown to be similar to short-term reproducibility, indicating that the ON does not change substantially within 11
months. The increased accuracy of this new technique provides increased power when searching for anatomical changes
in ON size amongst patient populations.

High-angular-resolution diffusion-weighted imaging (HARDI) MRI acquisitions have become common for use with
higher order models of diffusion. Despite successes in resolving complex fiber configurations and probing
microstructural properties of brain tissue, there is no common consensus on the optimal b-value and number of
diffusion directions to use for these HARDI methods. While this question has been addressed by analysis of the
diffusion-weighted signal directly, it is unclear how this translates to the information and metrics derived from the
HARDI models themselves. Using a high angular resolution data set acquired at a range of b-values, and repeated 11
times on a single subject, we study how the b-value and number of diffusion directions impacts the reproducibility
and precision of metrics derived from Q-ball imaging, a popular HARDI technique. We find that Q-ball metrics
associated with tissue microstructure and white matter fiber orientation are sensitive to both the number of diffusion
directions and the spherical harmonic representation of the Q-ball, and often are biased when under sampled. These
results can advise researchers on appropriate acquisition and processing schemes, particularly when it comes to
optimizing the number of diffusion directions needed for metrics derived from Q-ball imaging.

Autofluorescence microscopy of NAD(P)H and FAD provides functional metabolic measurements at the single-cell level.
Here, density-based clustering algorithms were applied to metabolic autofluorescence measurements to identify cell-level
heterogeneity in tumor cell cultures. The performance of the density-based clustering algorithm, DENCLUE, was tested
in samples with known heterogeneity (co-cultures of breast carcinoma lines). DENCLUE was found to better represent
the distribution of cell clusters compared to Gaussian mixture modeling. Overall, DENCLUE is a promising approach to
quantify cell-level heterogeneity, and could be used to understand single cell population dynamics in cancer progression
and treatment.

Active shape models (ASMs) have been widely used for extracting human anatomies in medical images given their capability for shape regularization of topology preservation. However, sensitivity to model initialization and local correspondence search often undermines their performances, especially around highly variable contexts in computed-tomography (CT) and magnetic resonance (MR) images. In this study, we propose an augmented ASM (AASM) by integrating the multiatlas label fusion (MALF) and level set (LS) techniques into the traditional ASM framework. Using AASM, landmark updates are optimized globally via a region-based LS evolution applied on the probability map generated from MALF. This augmentation effectively extends the searching range of correspondent landmarks while reducing sensitivity to the image contexts and improves the segmentation robustness. We propose the AASM framework as a two-dimensional segmentation technique targeting structures with one axis of regularity. We apply AASM approach to abdomen CT and spinal cord (SC) MR segmentation challenges. On 20 CT scans, the AASM segmentation of the whole abdominal wall enables the subcutaneous/visceral fat measurement, with high correlation to the measurement derived from manual segmentation. On 28 3T MR scans, AASM yields better performances than other state-of-the-art approaches in segmenting white/gray matter in SC.

Adopting high performance cloud computing for medical image processing is a popular trend given the pressing needs of large studies. Amazon Web Services (AWS) provide reliable, on-demand, and inexpensive cloud computing services. Our research objective is to implement an affordable, scalable and easy-to-use AWS framework for the Java Image Science Toolkit (JIST). JIST is a plugin for Medical- Image Processing, Analysis, and Visualization (MIPAV) that provides a graphical pipeline implementation allowing users to quickly test and develop pipelines. JIST is DRMAA-compliant allowing it to run on portable batch system grids. However, as new processing methods are implemented and developed, memory may often be a bottleneck for not only lab computers, but also possibly some local grids. Integrating JIST with the AWS cloud alleviates these possible restrictions and does not require users to have deep knowledge of programming in Java. Workflow definition/management and cloud configurations are two key challenges in this research. Using a simple unified control panel, users have the ability to set the numbers of nodes and select from a variety of pre-configured AWS EC2 nodes with different numbers of processors and memory storage. Intuitively, we configured Amazon S3 storage to be mounted by pay-for- use Amazon EC2 instances. Hence, S3 storage is recognized as a shared cloud resource. The Amazon EC2 instances provide pre-installs of all necessary packages to run JIST. This work presents an implementation that facilitates the integration of JIST with AWS. We describe the theoretical cost/benefit formulae to decide between local serial execution versus cloud computing and apply this analysis to an empirical diffusion tensor imaging pipeline.

The optic nerve (ON) plays a crucial role in human vision transporting all visual information from the retina to the brain for higher order processing. There are many diseases that affect the ON structure such as optic neuritis, anterior ischemic optic neuropathy and multiple sclerosis. Because the ON is the sole pathway for visual information from the retina to areas of higher level processing, measures of ON damage have been shown to correlate well with visual deficits. Increased intracranial pressure has been shown to correlate with the size of the cerebrospinal fluid (CSF) surrounding the ON. These measures are generally taken at an arbitrary point along the nerve and do not account for changes along the length of the ON. We propose a high contrast and high-resolution 3-D acquired isotropic imaging sequence optimized for ON imaging. We have acquired scan-rescan data using the optimized sequence and a current standard of care protocol for 10 subjects. We show that this sequence has superior contrast-to-noise ratio to the current standard of care while achieving a factor of 11 higher resolution. We apply a previously published automatic pipeline to segment the ON and CSF sheath and measure the size of each individually. We show that these measures of ON size have lower short- term reproducibility than the population variance and the variability along the length of the nerve. We find that the proposed imaging protocol is (1) useful in detecting population differences and local changes and (2) a promising tool for investigating biomarkers related to structural changes of the ON.

The abdominal wall is an important structure differentiating subcutaneous and visceral compartments and intimately involved with maintaining abdominal structure. Segmentation of the whole abdominal wall on routinely acquired computed tomography (CT) scans remains challenging due to variations and complexities of the wall and surrounding tissues. In this study, we propose a slice-wise augmented active shape model (AASM) approach to robustly segment both the outer and inner surfaces of the abdominal wall. Multi-atlas label fusion (MALF) and level set (LS) techniques are integrated into the traditional ASM framework. The AASM approach globally optimizes the landmark updates in the presence of complicated underlying local anatomical contexts. The proposed approach was validated on 184 axial slices of 20 CT scans. The Hausdorff distance against the manual segmentation was significantly reduced using proposed approach compared to that using ASM, MALF, and LS individually. Our segmentation of the whole abdominal wall enables the subcutaneous and visceral fat measurement, with high correlation to the measurement derived from manual segmentation. This study presents the first generic algorithm that combines ASM, MALF, and LS, and demonstrates practical application for automatically capturing visceral and subcutaneous fat volumes.

Early detection of risk is critical in determining the course of treatment in traumatic brain injury (TBI). Computed tomography (CT) acquired at admission has shown latent prognostic value in prior studies; however, no robust clinical risk predictions have been achieved based on the imaging data in large-scale TBI analysis. The major challenge lies in the lack of consistent and complete medical records for patients, and an inherent bias associated with the limited number of patients samples with high-risk outcomes in available TBI datasets. Herein, we propose a Bayesian framework with mutual information-based forward feature selection to handle this type of data. Using multi-atlas segmentation, 154 image-based features (capturing intensity, volume and texture) were computed over 22 ROIs in 1791 CT scans. These features were combined with 14 clinical parameters and converted into risk likelihood scores using Bayes modeling. We explore the prediction power of the image features versus the clinical measures for various risk outcomes. The imaging data alone were more predictive of outcomes than the clinical data (including Marshall CT classification) for discharge disposition with an area under the curve of 0.81 vs. 0.67, but less predictive than clinical data for discharge Glasgow Coma Scale (GCS) score with an area under the curve of 0.65 vs. 0.85. However, in both cases, combining imaging and clinical data increased the combined area under the curve with 0.86 for discharge disposition and 0.88 for discharge GCS score. In conclusion, CT data have meaningful prognostic value for TBI patients beyond what is captured in clinical measures and the Marshall CT classification.

Modern magnetic resonance imaging (MRI) brain atlases are high quality 3-D volumes with specific structures labeled in the volume. Atlases are essential in providing a common space for interpretation of results across studies, for anatomical education, and providing quantitative image-based navigation. Extensive work has been devoted to atlas construction for humans, macaque, and several non-primate species (e.g., rat). One notable gap in the literature is the common squirrel monkey – for which the primary published atlases date from the 1960’s. The common squirrel monkey has been used extensively as surrogate for humans in biomedical studies, given its anatomical neuro-system similarities and practical considerations. This work describes the continued development of a multi-modal MRI atlas for the common squirrel monkey, for which a structural imaging space and gray matter parcels have been previously constructed. This study adds white matter tracts to the atlas. The new atlas includes 49 white matter (WM) tracts, defined using diffusion tensor imaging (DTI) in three animals and combines these data to define the anatomical locations of these tracks in a standardized coordinate system compatible with previous development. An anatomist reviewed the resulting tracts and the inter-animal reproducibility (i.e., the Dice index of each WM parcel across animals in common space) was assessed. The Dice indices range from 0.05 to 0.80 due to differences of local registration quality and the variation of WM tract position across individuals. However, the combined WM labels from the 3 animals represent the general locations of WM parcels, adding basic connectivity information to the atlas.

Pathologies of the optic nerve and orbit impact millions of Americans and quantitative assessment of the orbital structures on 3-D imaging would provide objective markers to enhance diagnostic accuracy, improve timely intervention, and eventually preserve visual function. Recent studies have shown that the multi-atlas methodology is suitable for identifying orbital structures, but challenges arise in the identification of the individual extraocular rectus muscles that control eye movement. This is increasingly problematic in diseased eyes, where these muscles often appear to fuse at the back of the orbit (at the resolution of clinical computed tomography imaging) due to inflammation or crowding. We propose the use of Kalman filters to track the muscles in three-dimensions to refine multi-atlas segmentation and resolve ambiguity due to imaging resolution, noise, and artifacts. The purpose of our study is to investigate a method of automatically generating orbital metrics from CT imaging and demonstrate the utility of the approach by correlating structural metrics of the eye orbit with clinical data and visual function measures in subjects with thyroid eye disease. The pilot study demonstrates that automatically calculated orbital metrics are strongly correlated with several clinical characteristics. Moreover, it is shown that the superior, inferior, medial and lateral rectus muscles obtained using Kalman filters are each correlated with different categories of functional deficit. These findings serve as foundation for further investigation in the use of CT imaging in the study, analysis and diagnosis of ocular diseases, specifically thyroid eye disease.

The cerebellum is a somatotopically organized central component of the central nervous system well known to be involved with motor coordination and increasingly recognized roles in cognition and planning. Recent work in multi-atlas labeling has created methods that offer the potential for fully automated 3-D parcellation of the cerebellar lobules and vermis (which are organizationally equivalent to cortical gray matter areas). This work explores the trade offs of using different statistical fusion techniques and post hoc optimizations in two datasets with distinct imaging protocols. We offer a novel fusion technique by extending the ideas of the Selective and Iterative Method for Performance Level Estimation (SIMPLE) to a patch-based performance model. We demonstrate the effectiveness of our algorithm, Non-Local SIMPLE, for segmentation of a mixed population of healthy subjects and patients with severe cerebellar anatomy. Under the first imaging protocol, we show that Non-Local SIMPLE outperforms previous gold-standard segmentation techniques. In the second imaging protocol, we show that Non-Local SIMPLE outperforms previous gold standard techniques but is outperformed by a non-locally weighted vote with the deeper population of atlases available. This work advances the state of the art in open source cerebellar segmentation algorithms and offers the opportunity for routinely including cerebellar segmentation in magnetic resonance imaging studies that acquire whole brain T1-weighted volumes with approximately 1 mm isotropic resolution.

Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this “segmentation to surface to parcellation” strategy has shown limitation in various situations. In this work, we propose a novel “multi-atlas segmentation to surface” method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

T1-weighted magnetic resonance imaging (MRI) generates contrasts with primary sensitivity to local T1 properties (with lesser T2 and PD contributions). The observed signal intensity is determined by these local properties and the sequence parameters of the acquisition. In common practice, a range of acceptable parameters is used to ensure “similar” contrast across scanners used for any particular study (e.g., the ADNI standard MPRAGE). However, different studies may use different ranges of parameters and report the derived data as simply “T1-weighted”. Physics and imaging authors pay strong heed to the specifics of the imaging sequences, but image processing authors have historically been more lax. Herein, we consider three T1-weighted sequences acquired the same underlying protocol (MPRAGE) and vendor (Philips), but “normal study-to-study variation” in parameters. We show that the gray matter/white matter/cerebrospinal fluid contrast is subtly but systemically different between these images and yields systemically different measurements of brain volume. The problem derives from the visually apparent boundary shifts, which would also be seen by a human rater. We present and evaluate two solutions to produce consistent segmentation results across imaging protocols. First, we propose to acquire multiple sequences on a subset of the data and use the multi-modal imaging as atlases to segment target images any of the available sequences. Second (if additional imaging is not available), we propose to synthesize atlases of the target imaging sequence and use the synthesized atlases in place of atlas imaging data. Both approaches significantly improve consistency of target labeling.

Identifying cross-sectional and longitudinal correspondence in the abdomen on computed tomography (CT) scans is necessary for quantitatively tracking change and understanding population characteristics, yet abdominal image registration is a challenging problem. The key difficulty in solving this problem is huge variations in organ dimensions and shapes across subjects. The current standard registration method uses the global or body-wise registration technique, which is based on the global topology for alignment. This method (although producing decent results) has substantial influence of outliers, thus leaving room for significant improvement. Here, we study a new image registration approach using local (organ-wise registration) by first creating organ-specific bounding boxes and then using these regions of interest (ROIs) for aligning references to target. Based on Dice Similarity Coefficient (DSC), Mean Surface Distance (MSD) and Hausdorff Distance (HD), the organ-wise approach is demonstrated to have significantly better results by minimizing the distorting effects of organ variations. This paper compares exclusively the two registration methods by providing novel quantitative and qualitative comparison data and is a subset of the more comprehensive problem of improving the multi-atlas segmentation by using organ normalization.

A revised version of this paper, published originally on 17 March 2015, was published on 2 July 2015, replacing the original paper. The text of the first paragraph of Section 2.2.2 has been revised and two additional references have been added. The text of the first paragraph of Section 2.3 has also been revised. The revised paper is available at http://dx.doi.org/10.1117/12.2081443.

Optic neuritis is a sudden inflammation of the optic nerve (ON) and is marked by pain on eye movement, and visual symptoms such as a decrease in visual acuity, color vision, contrast and visual field defects. The ON is closely linked with multiple sclerosis (MS) and patients have a 50% chance of developing MS within 15 years. Recent advances in multi-atlas segmentation methods have omitted volumetric assessment. In the past, measuring the size of the ON has been done by hand. We utilize a new method of automatically segmenting the ON to measure the radii of both the ON and surrounding cerebrospinal fluid (CSF) sheath to develop a normative distribution of healthy young adults. We examine this distribution for any trends and find that ON and CSF sheath radii do not vary between 20-35 years of age and between sexes. We evaluate how six patients suffering from optic neuropathy compare to this distribution of controls. We find that of these six patients, five of them qualitatively differ from the normative distribution which suggests this technique could be used in the future to distinguish between optic neuritis patients and healthy controls

Medical imaging plays a key role in guiding treatment of traumatic brain injury (TBI) and for diagnosing intracranial hemorrhage; most commonly rapid computed tomography (CT) imaging is performed. Outcomes for patients with TBI are variable and difficult to predict upon hospital admission. Quantitative outcome scales (e.g., the Marshall classification) have been proposed to grade TBI severity on CT, but such measures have had relatively low value in staging patients by prognosis. Herein, we examine a cohort of 1,003 subjects admitted for TBI and imaged clinically to identify potential prognostic metrics using a “big data” paradigm. For all patients, a brain scan was segmented with multi-atlas labeling, and intensity/volume/texture features were computed in a localized manner. In a 10-fold crossvalidation approach, the explanatory value of the image-derived features is assessed for length of hospital stay (days), discharge disposition (five point scale from death to return home), and the Rancho Los Amigos functional outcome score (Rancho Score). Image-derived features increased the predictive R<sup>2</sup> to 0.38 (from 0.18) for length of stay, to 0.51 (from 0.4) for discharge disposition, and to 0.31 (from 0.16) for Rancho Score (over models consisting only of non-imaging admission metrics, but including positive/negative radiological CT findings). This study demonstrates that high volume retrospective analysis of clinical imaging data can reveal imaging signatures with prognostic value. These targets are suited for follow-up validation and represent targets for future feature selection efforts. Moreover, the increase in prognostic value would improve staging for intervention assessment and provide more reliable guidance for patients.

Multi-atlas labeling has come in wide spread use for whole brain labeling on magnetic resonance imaging. Recent challenges have shown that leading techniques are near (or at) human expert reproducibility for cortical gray matter labels. However, these approaches tend to treat white matter as essentially homogeneous (as white matter exhibits isointense signal on structural MRI). The state-of-the-art for white matter atlas is the single-subject Johns Hopkins Eve atlas. Numerous approaches have attempted to use tractography and/or orientation information to identify homologous white matter structures across subjects. Despite success with large tracts, these approaches have been plagued by difficulties in with subtle differences in course, low signal to noise, and complex structural relationships for smaller tracts. Here, we investigate use of atlas-based labeling to propagate the Eve atlas to unlabeled datasets. We evaluate single atlas labeling and multi-atlas labeling using synthetic atlases derived from the single manually labeled atlas. On 5 representative tracts for 10 subjects, we demonstrate that (1) single atlas labeling generally provides segmentations within 2mm mean surface distance, (2) morphologically constraining DTI labels within structural MRI white matter reduces variability, and (3) multi-atlas labeling did not improve accuracy. These efforts present a preliminary indication that single atlas labels with correction is reasonable, but caution should be applied. To purse multi-atlas labeling and more fully characterize overall performance, more labeled datasets would be necessary.

Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining.

Image registration has become an essential image processing technique to compare data across time and individuals. With the successes in volumetric brain registration, general-purpose software tools are beginning to be applied to abdominal computed tomography (CT) scans. Herein, we evaluate five current tools for registering clinically acquired abdominal CT scans. Twelve abdominal organs were labeled on a set of 20 atlases to enable assessment of correspondence. The 20 atlases were pairwise registered based on only intensity information with five registration tools (affine IRTK, FNIRT, Non-Rigid IRTK, NiftyReg, and ANTs). Following the brain literature, the Dice similarity coefficient (DSC), mean surface distance, and Hausdorff distance were calculated on the registered organs individually. However, interpretation was confounded due to a significant proportion of outliers. Examining the retrospectively selected top 1 and 5 atlases for each target revealed that there was a substantive performance difference between methods. To further our understanding, we constructed majority vote segmentation with the top 5 DSC values for each organ and target. The results illustrated a median improvement of 85% in DSC between the raw results and majority vote. These experiments show that some images may be well registered to some targets using the available software tools, but there is significant room for improvement and reveals the need for innovation and research in the field of registration in abdominal CTs. If image registration is to be used for local interpretation of abdominal CT, great care must be taken to account for outliers (e.g., atlas selection in statistical fusion).

Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128x128 to an output encoded layer of 4x384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This preliminary effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques.

This effort is a continuation of development of a digital brain atlas of the common squirrel monkey, <i>Saimiri sciureus</i>, a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. Here, we present the integration of histology with multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. The central concept of this work is to use block face photography to establish an intermediate common space in coordinate system which preserves the high resolution in-plane resolution of histology while enabling 3-D correspondence with MRI. <i>In vivo </i>MRI acquisitions include high resolution T2 structural imaging (300 μm isotropic) and low resolution diffusion tensor imaging (600 um isotropic). Ex vivo MRI acquisitions include high resolution T2 structural imaging and high resolution diffusion tensor imaging (both 300 μm isotropic). Cortical regions were manually annotated on the co-registered volumes based on published histological sections in-plane. We describe mapping of histology and MRI based data of the common squirrel monkey and construction of a viewing tool that enable online viewing of these datasets. The previously descried atlas MRI is used for its deformation to provide accurate conformation to the MRI, thus adding information at the histological level to the MRI volume. This paper presents the mapping of single 2D image slice in block face as a proof of concept and this can be extended to map the atlas space in 3D coordinate system as part of the future work and can be loaded to an XNAT system for further use.

Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing increasing interest in the medical imaging community. Gaussian mixture models (GMM) have been extensively used through medical segmentation, most notably in the brain for cerebrospinal fluid / gray matter / white matter differentiation. Because abdominal CT exhibit strong localized intensity characteristics, GMM have recently been incorporated in multi-stage abdominal segmentation algorithms. In the context of variable abdominal anatomy and rich algorithms, it is difficult to assess the marginal contribution of GMM. Herein, we characterize the efficacy of an <i>a posteriori </i>framework that integrates GMM of organ-wise intensity likelihood with spatial priors from multiple target-specific registered labels. In our study, we first manually labeled 100 CT images. Then, we assigned 40 images to use as training data for constructing target-specific spatial priors and intensity likelihoods. The remaining 60 images were evaluated as test targets for segmenting 12 abdominal organs. The overlap between the true and the automatic segmentations was measured by Dice similarity coefficient (DSC). A median improvement of 145% was achieved by integrating the GMM intensity likelihood against the specific spatial prior. The proposed framework opens the opportunities for abdominal organ segmentation by efficiently using both the spatial and appearance information from the atlases, and creates a benchmark for large-scale automatic abdominal segmentation.

The optic nerve (ON) plays a critical role in many devastating pathological conditions. Segmentation of the ON has the ability to provide understanding of anatomical development and progression of diseases of the ON. Recently, methods have been proposed to segment the ON but progress toward full automation has been limited. We optimize registration and fusion methods for a new multi-atlas framework for automated segmentation of the ONs, eye globes, and muscles on clinically acquired computed tomography (CT) data. Briefly, the multi-atlas approach consists of determining a region of interest within each scan using affine registration, followed by nonrigid registration on reduced field of view atlases, and performing statistical fusion on the results. We evaluate the robustness of the approach by segmenting the ON structure in 501 clinically acquired CT scan volumes obtained from 183 subjects from a thyroid eye disease patient population. A subset of 30 scan volumes was manually labeled to assess accuracy and guide method choice. Of the 18 compared methods, the ANTS Symmetric Normalization registration and nonlocal spatial simultaneous truth and performance level estimation statistical fusion resulted in the best overall performance, resulting in a median Dice similarity coefficient of 0.77, which is comparable with inter-rater (human) reproducibility at 0.73.

Imaging genetics is an emerging methodological field that combines genetic information with medical imaging-derived metrics to understand how genetic factors impact observable phenotypes. In order for a trait to be a reasonable phenotype in an imaging genetics study, it must be heritable: at least some proportion of its variance must be due to genetic influences. The Sequential Oligogenic Linkage Analysis Routines (SOLAR) imaging genetics software can estimate the heritability of a trait in complex pedigrees. We investigate the ability of SOLAR to accurately estimate heritability and common environmental effects on simulated imaging phenotypes in various family structures. We found that heritability is reliably estimated with small family-based studies of 40 to 80 individuals, though subtle differences remain between the family structures. In an imaging application analysis, we found that with 80 subjects in any of the family structures, estimated heritability of white matter fractional anisotropy was biased by <10% for every region of interest. Results from these studies can be used when investigators are evaluating power in planning genetic analyzes.

Multi-atlas registration-based segmentation is a popular technique in the medical imaging community, used to transform anatomical and functional information from a set of atlases onto a new patient that lacks this information. The accuracy of the projected information on the target image is dependent on the quality of the registrations between the atlas images and the target image. Recently, we have developed a technique called AQUIRC that aims at estimating the error of a non-rigid registration at the local level and was shown to correlate to error in a simulated case. Herein, we extend upon this work by applying AQUIRC to atlas selection at the local level across multiple structures in cases in which non-rigid registration is difficult. AQUIRC is applied to 6 structures, the brainstem, optic chiasm, left and right optic nerves, and the left and right eyes. We compare the results of AQUIRC to that of popular techniques, including Majority Vote, STAPLE, Non-Local STAPLE, and Locally-Weighted Vote. We show that AQUIRC can be used as a method to combine multiple segmentations and increase the accuracy of the projected information on a target image, and is comparable to cutting edge methods in the multi-atlas segmentation field.

The optic nerve is a sensitive central nervous system structure, which plays a critical role in many devastating pathological conditions. Several methods have been proposed in recent years to segment the optic nerve automatically, but progress toward full automation has been limited. Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. Herein we evaluate a framework for robust and fully automated segmentation of the optic nerves, eye globes and muscles. We employ a robust registration procedure for accurate registrations, variable voxel resolution and image fieldof- view. We demonstrate the efficacy of an optimal combination of SyN registration and a recently proposed label fusion algorithm (Non-local Spatial STAPLE) that accounts for small-scale errors in registration correspondence. On a dataset containing 30 highly varying computed tomography (CT) images of the human brain, the optimal registration and label fusion pipeline resulted in a median Dice similarity coefficient of 0.77, symmetric mean surface distance error of 0.55 mm, symmetric Hausdorff distance error of 3.33 mm for the optic nerves. Simultaneously, we demonstrate the robustness of the optimal algorithm by segmenting the optic nerve structure in 316 CT scans obtained from 182 subjects from a thyroid eye disease (TED) patient population.

Spleen segmentation on clinically acquired CT data is a challenging problem given the complicity and variability of abdominal anatomy. Multi-atlas segmentation is a potential method for robust estimation of spleen segmentations, but can be negatively impacted by registration errors. Although labeled atlases explicitly capture information related to feasible organ shapes, multi-atlas methods have largely used this information implicitly through registration. We propose to integrate a level set shape model into the traditional label fusion framework to create a shape-constrained multi-atlas segmentation framework. Briefly, we (1) adapt two alternative atlas-to-target registrations to obtain the loose bounds on the inner and outer boundaries of the spleen shape, (2) project the fusion estimate to registered shape models, and (3) convert the projected shape into shape priors. With the constraint of the shape prior, our proposed method offers a statistically significant improvement in spleen labeling accuracy with an increase in DSC by 0.06, a decrease in symmetric mean surface distance by 4.01 mm, and a decrease in symmetric Hausdorff surface distance by 23.21 mm when compared to a locally weighted vote (LWV) method.

Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally – fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance. Building on the seminal work in statistical fusion, we reformulate the traditional rater performance model from a multi-tiered hierarchical perspective. This new approach provides a natural framework for leveraging known anatomical relationships and accurately modeling the types of errors that raters (or atlases) make within a hierarchically consistent formulation. Herein, we describe several contributions. First, we derive a theoretical advancement to the statistical fusion framework that enables the simultaneous estimation of multiple (hierarchical) performance models within the statistical fusion context. Second, we demonstrate that the proposed hierarchical formulation is highly amenable to the state-of-the-art advancements that have been made to the statistical fusion framework. Lastly, in an empirical whole-brain segmentation task we demonstrate substantial qualitative and significant quantitative improvement in overall segmentation accuracy.

Imaging genetics is an emerging methodology that combines genetic information with imaging-derived metrics to understand how genetic factors impact observable structural, functional, and quantitative phenotypes. Many of the most well-known genetic studies are based on Genome-Wide Association Studies (GWAS), which use large populations of related or unrelated individuals to associate traits and disorders with individual genetic factors. Merging imaging and genetics may potentially lead to improved power of association in GWAS because imaging traits may be more sensitive phenotypes, being closer to underlying genetic mechanisms, and their quantitative nature inherently increases power. We are developing SOLAR-ECLIPSE (SE) imaging genetics software which is capable of performing genetic analyses with both large-scale quantitative trait data and family structures of variable complexity. This program can estimate the contribution of genetic commonality among related subjects to a given phenotype, and essentially answer the question of whether or not the phenotype is <i>heritable</i>. This central factor of interest, <i>heritability</i>, offers bounds on the direct genetic influence over observed phenotypes. In order for a trait to be a good phenotype for GWAS, it must be heritable: at least some proportion of its variance must be due to genetic influences. A variety of family structures are commonly used for estimating heritability, yet the variability and biases for each as a function of the sample size are unknown. Herein, we investigate the ability of SOLAR to accurately estimate heritability models based on imaging data simulated using Monte Carlo methods implemented in R. We characterize the bias and the variability of heritability estimates from SOLAR as a function of sample size and pedigree structure (including twins, nuclear families, and nuclear families with grandparents).

The common squirrel monkey, <i>Saimiri sciureus</i>, is a New World monkey with functional and microstructural organization of central nervous system similar to that of humans. It is one of the most commonly used South American primates in biomedical research. Unlike its Old World macaque cousins, no digital atlases have described the organization of the squirrel monkey brain. Here, we present a multi-modal magnetic resonance imaging (MRI) atlas constructed from the brain of an adult female squirrel monkey. <i>In vivo</i> MRI acquisitions include high resolution T2 structural imaging and low resolution diffusion tensor imaging. <i>Ex vivo</i> MRI acquisitions include high resolution T2 structural imaging and high resolution diffusion tensor imaging. Cortical regions were manually annotated on the co-registered volumes based on published histological sections.

Anatomical contexts (spatial labels) are critical for interpretation of medical imaging content. Numerous approaches have been devised for segmentation, query, and retrieval within the Picture Archive and Communication System (PACS) framework. To date, application-based methods for anatomical localization and tissue classification have yielded the most successful results, but these approaches typically rely upon the availability of standardized imaging sequences. With the ever expanding scope of PACS archives — including multiple imaging modalities, multiple image types within a modality, and multi-site efforts, it is becoming increasingly burdensome to devise a specific method for each data type. To address the challenge of generalizing segmentations from one modality to another, we consider multi-atlas segmentation to transfer label information from labeled T1-weighted MRI data to unlabeled data collected in a diffusion tensor imaging (DTI) experiment. The label transfer approach is fully automated and enables a generalizable cross-modality segmentation method. Herein, we propose a multi-tier multi-atlas segmentation framework for the segmentation of previously unlabeled imaging modalities (e.g.,<i>B</i><sub>0</sub> images for DTI analysis). We show that this approach can be used to construct informed structure-wise noise estimates for fractional anisotropy (FA) measurements of DTI. Although this label transfer methodology is demonstrated in the context of quality control of DTI images, the proposed framework is applicable to any application where the segmentation of unlabeled modalities is limited due to the current collection of available atlases.

Traumatic brain injury (TBI) is an increasingly important public health concern. While there are several promising avenues of intervention, clinical assessments are relatively coarse and comparative quantitative analysis is an emerging field. Imaging data provide potentially useful information for evaluating TBI across functional, structural, and microstructural phenotypes. Integration and management of disparate data types are major obstacles. In a multi-institution collaboration, we are collecting electroencephalogy (EEG), structural MRI, diffusion tensor MRI (DTI), and single photon emission computed tomography (SPECT) from a large cohort of US Army service members exposed to mild or moderate TBI who are undergoing experimental treatment. We have constructed a robust informatics backbone for this project centered on the DICOM standard and eXtensible Neuroimaging Archive Toolkit (XNAT) server. Herein, we discuss (1) optimization of data transmission, validation and storage, (2) quality assurance and workflow management, and (3) integration of high performance computing with research software.

Immersive virtual environments use a stereoscopic head-mounted display and data glove to create high fidelity virtual experiences in which users can interact with three-dimensional models and perceive relationships at their true scale. This stands in stark contrast to traditional PACS-based infrastructure in which images are viewed as stacks of two dimensional slices, or, at best, disembodied renderings. Although there has substantial innovation in immersive virtual environments for entertainment and consumer media, these technologies have not been widely applied in clinical applications. Here, we consider potential applications of immersive virtual environments for ventral hernia patients with abdominal computed tomography imaging data. Nearly a half million ventral hernias occur in the United States each year, and hernia repair is the most commonly performed general surgery operation worldwide. A significant problem in these conditions is communicating the urgency, degree of severity, and impact of a hernia (and potential repair) on patient quality of life. Hernias are defined by ruptures in the abdominal wall (i.e., the absence of healthy tissues) rather than a growth (e.g., cancer); therefore, understanding a hernia necessitates understanding the entire abdomen. Our environment allows surgeons and patients to view body scans at scale and interact with these virtual models using a data glove. This visualization and interaction allows users to perceive the relationship between physical structures and medical imaging data. The system provides close integration of PACS-based CT data with immersive virtual environments and creates opportunities to study and optimize interfaces for patient communication, operative planning, and medical education.

Ventral hernias (VHs) are abnormal openings in the anterior abdominal wall that are common side effects of surgical intervention. Repair of VHs is the most commonly performed procedure by general surgeons worldwide, but VH repair outcomes are not particularly encouraging (with recurrence rates up to 43%). A variety of open and laparoscopic techniques are available for hernia repair, and the specific technique used is ultimately driven by surgeon preference and experience. Despite routine acquisition of computed tomography (CT) for VH patients, little quantitative information is available on which to guide selection of a particular approach and/or optimize patient-specific treatment. From anecdotal interviews, the success of VH repair procedures correlates with hernia size, location, and involvement of secondary structures. Herein, we propose an image labeling protocol to segment the anterior abdominal area to provide a geometric basis with which to derive biomarkers and evaluate treatment efficacy. Based on routine clinical CT data, we are able to identify inner and outer surfaces of the abdominal walls and the herniated volume. This is the first formal presentation of a protocol to quantify these structures on abdominal CT. The intra- and inter rater reproducibilities of this protocol are evaluated on 4 patients with suspected VH (3 patients were ultimately diagnosed with VH while 1 was not). Mean surfaces distances of less than 2mm were achieved for all structures.

Labeling or segmentation of structures of interest on medical images plays an essential role in both clinical and scientific
understanding of the biological etiology, progression, and recurrence of pathological disorders. Here, we focus on the
optic nerve, a structure that plays a critical role in many devastating pathological conditions – including glaucoma,
ischemic neuropathy, optic neuritis and multiple-sclerosis. Ideally, existing fully automated procedures would result in
accurate and robust segmentation of the optic nerve anatomy. However, current segmentation procedures often require
manual intervention due to anatomical and imaging variability. Herein, we propose a framework for robust and fully-automated
segmentation of the optic nerve anatomy. First, we provide a robust registration procedure that results in
consistent registrations, despite highly varying data in terms of voxel resolution and image field-of-view. Additionally,
we demonstrate the efficacy of a recently proposed non-local label fusion algorithm that accounts for small scale errors
in registration correspondence. On a dataset consisting of 31 highly varying computed tomography (CT) images of the
human brain, we demonstrate that the proposed framework consistently results in accurate segmentations. In particular,
we show (1) that the proposed registration procedure results in robust registrations of the optic nerve anatomy, and (2)
that the non-local statistical fusion algorithm significantly outperforms several of the state-of-the-art label fusion
algorithms.

The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is
fraught with failure; recurrence rates ranging from 24-43% have been reported, even with the use of biocompatible
mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical
judgments; notably, quantitative metrics based on image-processing are not used. We propose that image segmentation
methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation
on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention. To
date, automated segmentation algorithms have not been presented to quantify the abdominal wall and potential hernias.
In this pilot study with four clinically acquired CT scans on post-operative patients, we demonstrate a novel approach to
geometric classification of the abdominal wall and essential abdominal features (including bony landmarks and skin
surfaces). Our approach uses a hierarchical design in which the abdominal wall is isolated in the context of the skin and
bony structures using level set methods. All segmentation results were quantitatively validated with surface errors based
on manually labeled ground truth. Mean surface errors for the outer surface of the abdominal wall was less than 2mm.
This approach establishes a baseline for characterizing the abdominal wall for improving VH care.

Malignant gliomas are the most common form of primary neoplasm in the central nervous system, and one of the
most rapidly fatal of all human malignancies. They are treated by maximal surgical resection followed by radiation
and chemotherapy. Herein, we seek to improve the methods available to quantify the extent of tumors using newly
presented, collaborative labeling techniques on magnetic resonance imaging. Traditionally, labeling medical images
has entailed that expert raters operate on one image at a time, which is resource intensive and not practical for very
large datasets. Using many, minimally trained raters to label images has the possibility of minimizing laboratory
requirements and allowing high degrees of parallelism. A successful effort also has the possibility of reducing
overall cost. This potentially transformative technology presents a new set of problems, because one must pose the
labeling challenge in a manner accessible to people with little or no background in labeling medical images and
raters cannot be expected to read detailed instructions. Hence, a different training method has to be employed. The
training must appeal to all types of learners and have the same concepts presented in multiple ways to ensure that all
the subjects understand the basics of labeling. Our overall objective is to demonstrate the feasibility of studying
malignant glioma morphometry through statistical analysis of the collaborative efforts of many, minimally-trained
raters. This study presents preliminary results on optimization of the WebMILL framework for neoplasm labeling
and investigates the initial contributions of 78 raters labeling 98 whole-brain datasets.

In deep brain stimulation surgeries, stimulating electrodes are placed at specific targets in the deep brain to treat
neurological disorders. Reaching these targets safely requires avoiding critical structures in the brain. Meticulous
planning is required to find a safe path from the cortical surface to the intended target. Choosing a trajectory
automatically is difficult because there is little consensus among neurosurgeons on what is optimal. Our goals are to
design a path planning system that is able to learn the preferences of individual surgeons and, eventually, to standardize
the surgical approach using this learned information. In this work, we take the first step towards these goals, which is to
develop a trajectory planning approach that is able to effectively mimic individual surgeons and is designed such that
parameters, which potentially can be automatically learned, are used to describe an individual surgeon's preferences. To
validate the approach, two neurosurgeons were asked to choose between their manual and a computed trajectory, blinded
to their identity. The results of this experiment showed that the neurosurgeons preferred the computed trajectory over
their own in 10 out of 40 cases. The computed trajectory was judged to be equivalent to the manual one or otherwise
acceptable in 27 of the remaining cases. These results demonstrate the potential clinical utility of computer-assisted path
planning.

Precise image acquisition is an integral part of modern patient care and medical imaging research. Periodic quality
control using standardized protocols and phantoms ensures that scanners are operating according to specifications, yet
such procedures do not ensure that individual datasets are free from corruption; for example due to patient motion,
transient interference, or physiological variability. If unacceptable artifacts are noticed during scanning, a technologist
can repeat a procedure. Yet, substantial delays may be incurred if a problematic scan is not noticed until a radiologist
reads the scans or an automated algorithm fails. Given scores of slices in typical three-dimensional scans and widevariety
of potential use cases, a technologist cannot practically be expected inspect all images. In large-scale research,
automated pipeline systems have had great success in achieving high throughput. However, clinical and institutional
workflows are largely based on DICOM and PACS technologies; these systems are not readily compatible with research
systems due to security and privacy restrictions. Hence, quantitative quality control has been relegated to individual
investigators and too often neglected. Herein, we propose a scalable system, the Vanderbilt Image Processing Enterprise
Resource (VIPER) to integrate modular quality control and image analysis routines with a standard PACS
configuration. This server unifies image processing routines across an institutional level and provides a simple interface
so that investigators can collaborate to deploy new analysis technologies. VIPER integrates with high performance
computing environments has successfully analyzed all standard scans from our institutional research center over the
course of the last 18 months.

Image labeling is an essential step for quantitative analysis of medical images. Many image labeling algorithms require
seed identification in order to initialize segmentation algorithms such as region growing, graph cuts, and the random
walker. Seeds are usually placed manually by human raters, which makes these algorithms semi-automatic and can be
prohibitive for very large datasets. In this paper an automatic algorithm for placing seeds using multi-atlas registration
and statistical fusion is proposed. Atlases containing the centers of mass of a collection of neuroanatomical objects are
deformably registered in a training set to determine where these centers of mass go after labels transformed by
registration. The biases of these transformations are determined and incorporated in a continuous form of Simultaneous
Truth And Performance Level Estimation (STAPLE) fusion, thereby improving the estimates (on average) over a single
registration strategy that does not incorporate bias or fusion. We evaluate this technique using real 3D brain MR image
atlases and demonstrate its efficacy on correcting the data bias and reducing the fusion error.

Ultra-high field 7T magnetic resonance imaging (MRI) offers potentially unprecedented spatial resolution of functional
activity within the human brain through increased signal and contrast to noise ratios over traditional 1.5T and 3T MRI
scanners. However, the effects physiological and imaging artifacts are also greatly increased. Traditional statistical
parametric mapping theories based on distributional properties representative of data acquired at lower fields may be
inadequate for new 7T data. Herein, we investigate the model fitting residuals based on two 7T and one 3T protocols.
We find that model residuals are substantively more non-Gaussian at 7T relative to 3T. Imaging slices that passed
through regions with peak inhomogeneity problems (e.g., mid-brain acquisitions for the 7T hippocampus) exhibited
visually higher degrees of distortion along with spatially correlated and extreme values of kurtosis (a measure of non-
Gaussianity). The impacts of artifacts have been previously addressed for 3T data by estimating the covariance matrix of
the regression errors. We further extend the robust estimation approach for autoregressive models and evaluate the
qualitative impacts of this technique relative to traditional inference. Clear differences in statistical significance are
shown between inferences based on classical versus robust assumptions, which suggest that inferences based on
Gaussian assumptions are subject to practical (as well as theoretical) concerns regarding their power and validity. Hence,
modern statistical approaches, such as the robust autoregressive model posed herein, are appropriate and suitable for
inference with ultra-high field functional magnetic resonance imaging.

Quality and consistency of clinical and research data collected from Magnetic Resonance Imaging (MRI) scanners may
become suspect due to a wide variety of common factors including, experimental changes, hardware degradation,
hardware replacement, software updates, personnel changes, and observed imaging artifacts. Standard practice limits
quality analysis to visual assessment by a researcher/clinician or a quantitative quality control based upon phantoms
which may not be timely, cannot account for differing experimental protocol (e.g. gradient timings and strengths), and
may not be pertinent to the data or experimental question at hand. This paper presents a parallel processing pipeline
developed towards experiment specific automatic quantitative quality control of MRI data using diffusion tensor
imaging (DTI) as an experimental test case. The pipeline consists of automatic identification of DTI scans run on the
MRI scanner, calculation of DTI contrasts from the data, implementation of modern statistical methods (wild bootstrap
and SIMEX) to assess variance and bias in DTI contrasts, and quality assessment via power calculations and normative
values. For this pipeline, a DTI specific power calculation analysis is developed as well as the first incorporation of bias
estimates in DTI data to improve statistical analysis.

Segmentation plays a critical role in exposing connections between biological structure and function. The process of
label fusion collects and combines multiple observations into a single estimate. Statistically driven techniques provide
mechanisms to optimally combine segmentations; yet, optimality hinges upon accurate modeling of rater behavior.
Traditional approaches, e.g., Majority Vote and Simultaneous Truth and Performance Level Estimation (STAPLE), have
been shown to yield excellent performance in some cases, but do not account for spatial dependences of rater
performance (i.e., regional task difficulty). Recently, the COnsensus Level, Labeler Accuracy and Truth Estimation
(COLLATE) label fusion technique augmented the seminal STAPLE approach to simultaneously estimate regions of
relative consensus versus confusion along with rater performance. Herein, we extend the COLLATE framework to
account for multiple consensus levels. Toward this end, we posit a generalized model of rater behavior of which
Majority Vote, STAPLE, STAPLE Ignoring Consensus Voxels, and COLLATE are special cases. The new algorithm is
evaluated with simulations and shown to yield improved performance in cases with complex region difficulties. Multi-COLLATE achieve these results by capturing different consensus levels. The potential impacts and applications of
generative model to label fusion problems are discussed.

Labeling or segmentation of structures of interest in medical imaging plays an essential role in both clinical and
scientific understanding. Two of the common techniques to obtain these labels are through either fully automated
segmentation or through multi-atlas based segmentation and label fusion. Fully automated techniques often result in
highly accurate segmentations but lack the robustness to be viable in many cases. On the other hand, label fusion
techniques are often extremely robust, but lack the accuracy of automated algorithms for specific classes of problems.
Herein, we propose to perform simultaneous automated segmentation and statistical label fusion through the
reformulation of a generative model to include a linkage structure that explicitly estimates the complex global
relationships between labels and intensities. These relationships are inferred from the atlas labels and intensities and
applied to the target using a non-parametric approach. The novelty of this approach lies in the combination of previously
exclusive techniques and attempts to combine the accuracy benefits of automated segmentation with the robustness of a
multi-atlas based approach. The accuracy benefits of this simultaneous approach are assessed using a multi-label multi-atlas
whole-brain segmentation experiment and the segmentation of the highly variable thyroid on computed tomography
images. The results demonstrate that this technique has major benefits for certain types of problems and has the potential
to provide a paradigm shift in which the lines between statistical label fusion and automated segmentation are
dramatically blurred.

Exploitation of advanced, PACS-centric image analysis and interpretation pipelines provides well-developed storage,
retrieval, and archival capabilities along with state-of-the-art data providence, visualization, and clinical collaboration
technologies. However, pursuit of integrated medical imaging analysis through a PACS environment can be limiting in
terms of the overhead required to validate, evaluate and integrate emerging research technologies. Herein, we address
this challenge through presentation of a high-throughput bundled resource imaging system (HUBRIS) as an extension to
the Philips Research Imaging Development Environment (PRIDE). HUBRIS enables PACS-connected medical imaging
equipment to invoke tools provided by the Java Imaging Science Toolkit (JIST) so that a medical imaging platform (e.g.,
a magnetic resonance imaging scanner) can pass images and parameters to a server, which communicates with a grid
computing facility to invoke the selected algorithms. Generated images are passed back to the server and subsequently to
the imaging platform from which the images can be sent to a PACS. JIST makes use of an open application program
interface layer so that research technologies can be implemented in any language capable of communicating through a
system shell environment (e.g., Matlab, Java, C/C++, Perl, LISP, etc.). As demonstrated in this proof-of-concept
approach, HUBRIS enables evaluation and analysis of emerging technologies within well-developed PACS systems with
minimal adaptation of research software, which simplifies evaluation of new technologies in clinical research and
provides a more convenient use of PACS technology by imaging scientists.

Diffusion tensor imaging (DTI) is an MR imaging technique that uses a set of diffusion weighted measurements in order
to determine the water diffusion tensor at each voxel. In DTI, a single dominant fiber orientation is calculated at each
measured voxel, even if multiple populations of fibers are present within this voxel. A new approach called Crossing
Fiber Angular Resolution of Intra-voxel structure (CFARI) for processing diffusion weighted magnetic resonance data
has been recently introduced. Based on compressed sensing, CFARI is able to resolve intra-voxel structure from limited
number of measurements, but its performance as a function of the scan and algorithm parameters is poorly understood at
present. This paper describes simulation experiments to help understand CFARI performance tradeoffs as a function of
the data signal-to-noise ratio and the algorithm regularization parameter. In the compressed sensing criterion, the choice
of the regularization parameter &#946; is critical. If &#946; is too small, then the solution is the conventional least squares solution,
while if &#946; is too large then the solution is identically zero. The correct selection of &#946; turns out to be data dependent,
which means that it is also spatially varying. In this paper, simulations using two random tensors with different
diffusivities having the same fractional anisotropy but with different principle eigenvalues are carried out. Results reveal
that for a fixed scan time, acquisition of repeated measurements can improve CFARI performance and that a spatially
variable, data adaptive regularization parameter is beneficial in stabilizing results.

Mapping the quantitative relationship between structure and function in the human brain is an important and challenging
problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed
to statistically assess potential correlations between imaging and non-imaging metrics. Recently, biological parametric
mapping has extended the widely popular statistical parametric approach to enable application of the general linear
model to multiple image modalities (both for regressors and regressands) along with scalar valued observations.
This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple
imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers
and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy
between subjects. To enable widespread application of this approach, we introduce robust regression and robust inference
in the neuroimaging context of application of the general linear model. Through simulation and empirical studies,
we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The
robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations
between structural and functional neuroimaging modalities.

Labeling or parcellation of structures of interest on magnetic resonance imaging (MRI) is essential in quantifying and
characterizing correlation with numerous clinically relevant conditions. The use of statistical methods with automated
techniques or complete data sets from several different raters has been proposed to simultaneously estimate both rater
reliability and true labels. An extension to these statistical based methodologies was proposed that allowed for missing
labels, repeated labels and training trials. Herein, we present and demonstrate the viability of these statistical based
methodologies using real world data contributed by minimally trained human raters. The consistency of the statistical
estimates, the accuracy compared to the individual observations and the variability of both the estimates and the
individual observations with respect to the number of labels are discussed. It is demonstrated that the Gaussian based
statistical approach using the previously presented extensions successfully performs label fusion in a variety of contexts
using data from online (Internet-based) collaborations among minimally trained raters. This first successful
demonstration of a statistically based approach using "wild-type" data opens numerous possibilities for very large scale
efforts in collaboration. Extension and generalization of these technologies for new application spaces will certainly
present fascinating areas for continuing research.

Image labeling is an essential task for evaluating and analyzing morphometric features in medical imaging data. Labels
can be obtained by either human interaction or automated segmentation algorithms. However, both approaches for
labeling suffer from inevitable error due to noise and artifact in the acquired data. The Simultaneous Truth And
Performance Level Estimation (STAPLE) algorithm was developed to combine multiple rater decisions and
simultaneously estimate unobserved true labels as well as each rater's level of performance (i.e., reliability). A
generalization of STAPLE for the case of continuous-valued labels has also been proposed. In this paper, we first show
that with the proposed Gaussian distribution assumption, this continuous STAPLE formulation yields equivalent
likelihoods for the bias parameter, meaning that the bias parameter-one of the key performance indices-is actually
indeterminate. We resolve this ambiguity by augmenting the STAPLE expectation maximization formulation to include
<i>a priori</i> probabilities on the performance level parameters, which enables simultaneous, meaningful estimation of both
the rater bias and variance performance measures. We evaluate and demonstrate the efficacy of this approach in
simulations and also through a human rater experiment involving the identification the intersection points of the right
ventricle to the left ventricle in CINE cardiac data.

Labeling structures on medical images is crucial in determining clinically relevant correlations with morphometric and
volumetric features. For the exploration of new structures and new imaging modalities, validated automated methods do
not yet exist, and so researchers must rely on manually drawn landmarks. Voxel-by-voxel labeling can be extremely
resource intensive, so large-scale studies are problematic. Recently, statistical approaches and software have been
proposed to enable Internet-based collaborative labeling of medical images. While numerous labeling software tools
have been created, the use of these packages as high-throughput labeling systems has yet to become entirely viable given
training requirements. Herein, we explore two modifications to a typical mouse-based labeling system: (1) a platform
independent overlay for recognition of mouse gestures and (2) an inexpensive touch-screen tracking device for nonmouse
input. Through this study we characterize rater reliability in point, line, curve, and region placement. For the
mouse input, we find a placement accuracy of 2.48&plusmn;5.29 pixels (point), 0.630&plusmn;1.81 pixels (curve), 1.234&plusmn;6.99 pixels
(line), and 0.058&plusmn;0.027 (1 - Jaccard Index for region). The gesture software increased labeling speed by 27% overall
and accuracy by approximately 30-50% on point and line tracing tasks, but the touch screen module lead to slower and
more error prone labeling on all tasks, likely due to relatively poor sensitivity. In summary, the mouse gesture
integration layer runs as a seamless operating system overlay and could potentially benefit any labeling software; yet, the
inexpensive touch screen system requires improved usability optimization and calibration before it can provide an
efficient labeling system.

Studies of the size and morphology of anatomical structures rely on accurate and reproducible delineation of the structures,
obtained either by human raters or automatic segmentation algorithms. Measures of reproducibility and variability are
vital aspects of such studies and are usually estimated using repeated scans or repeated delineations (in the case of human
raters). Methods exist for simultaneously estimating the true structure and rater performance parameters from multiple
segmentations and have been demonstrated on volumetric images. In this work, we extend the applicability of previous
methods onto two-dimensional surfaces parameterized as triangle meshes. Label homogeneity is enforced using a Markov
random field formulated with an energy that addresses the challenges introduced by the surface parameterization. The
method was tested using both simulated raters and cortical gyral labels. Simulated raters are computed using a global
error model as well as a novel and more realistic boundary error model. We study the impact of raters and their accuracy
based on both models, and show how effectively this method estimates the true segmentation on simulated surfaces. The
Markov random field formulation was shown to effectively enforce homogeneity for raters suffering from label noise. We
demonstrated that our method provides substantial improvements in accuracy over single-atlas methods for all experimental
conditions.

Image labeling and parcellation are critical tasks for the assessment of volumetric and morphometric features in medical
imaging data. The process of image labeling is inherently error prone as images are corrupted by noise and artifact. Even
expert interpretations are subject to subjectivity and the precision of the individual raters. Hence, all labels must be
considered imperfect with some degree of inherent variability. One may seek multiple independent assessments to both
reduce this variability as well as quantify the degree of uncertainty. Existing techniques exploit maximum a posteriori
statistics to combine data from multiple raters. A current limitation with these approaches is that they require each rater
to generate a complete dataset, which is often impossible given both human foibles and the typical turnover rate of raters
in a research or clinical environment. Herein, we propose a robust set of extensions that allow for missing data, account
for repeated label sets, and utilize training/catch trial data. With these extensions, numerous raters can label small,
overlapping portions of a large dataset, and rater heterogeneity can be robustly controlled while simultaneously
estimating a single, reliable label set and characterizing uncertainty. The proposed approach enables parallel processing
of labeling tasks and reduces the otherwise detrimental impact of rater unavailability.

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Advanced PhotonicsJournal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews